CATFISHR: integration of transcriptome and inferred CNV profiles for comprehensive malignant cell identification
- 1NEXUS Personalized Health Technologies, ETH Zurich
Abstract
Malignant cell identification in single-cell RNA sequencing (scRNA-seq) remains challenging because transcriptomic cell typing, somatic variant detection and inferred copy-number approaches each have modality-specific limitations. Rare or ambiguous malignant populations are especially vulnerable to misclassification or exclusion, despite their relevance to treatment resistance, minimal residual disease, and metastasis. We developed CATFISHR, a semi-automated workflow that integrates RNA-expression similarity, inferred copy-number variation (CNV) profiles, and consensus cell typing to classify malignant cells relative to high-confidence references. CATFISHR was evaluated across eight cohorts, including seven scRNA-seq datasets spanning multiple carcinomas and melanoma subtypes, and one 10x Visium spatial transcriptomics sample. Compared with available benchmarks or ground truths, CATFISHR achieved balanced accuracy > 0.85 in six of eight cohorts and F1 score > 0.80 in six of eight cohorts. Cells uniquely identified by CATFISHR as malignant ("Hidden" cells) were present in every dataset and were not consistently explained by standard quality-control metrics. In several cohorts, Hidden cells were transcriptionally closer to benchmark malignant than non-malignant compartments; in Visium data, sampled Hidden spots contained histologist-confirmed morphologically malignant cells. CATFISHR reduces missed malignant calls while preserving rare, transitional and benchmark-discordant populations for downstream investigation.
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BibTeX
@misc{shukla2026catfishr,
title = {{CATFISHR: integration of transcriptome and inferred CNV profiles for comprehensive malignant cell identification}},
author = {P. Shukla and M. Mannino and M. Hofer and E. R. Lattmann and V. Haunerdinger and P. Turko and M. P. Levesque},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.22.760906},
url = {https://www.biorxiv.org/content/10.64898/2026.10.22.760906v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - CATFISHR: integration of transcriptome and inferred CNV profiles for comprehensive malignant cell identification
AU - P. Shukla
AU - M. Mannino
AU - M. Hofer
AU - E. R. Lattmann
AU - V. Haunerdinger
AU - P. Turko
AU - M. P. Levesque
PY - 2026
DA - 2026/10/22
PB - bioRxiv
DO - 10.64898/2026.10.22.760906
UR - https://www.biorxiv.org/content/10.64898/2026.10.22.760906v1
AB - Malignant cell identification in single-cell RNA sequencing (scRNA-seq) remains challenging because transcriptomic cell typing, somatic variant detection and inferred copy-number approaches each have modality-specific limitations. Rare or ambiguous malignant populations are especially vulnerable to misclassification or exclusion, despite their relevance to treatment resistance, minimal residual disease, and metastasis. We developed CATFISHR, a semi-automated workflow that integrates RNA-expression similarity, inferred copy-number variation (CNV) profiles, and consensus cell typing to classify malignant cells relative to high-confidence references. CATFISHR was evaluated across eight cohorts, including seven scRNA-seq datasets spanning multiple carcinomas and melanoma subtypes, and one 10x Visium spatial transcriptomics sample. Compared with available benchmarks or ground truths, CATFISHR achieved balanced accuracy > 0.85 in six of eight cohorts and F1 score > 0.80 in six of eight cohorts. Cells uniquely identified by CATFISHR as malignant ("Hidden" cells) were present in every dataset and were not consistently explained by standard quality-control metrics. In several cohorts, Hidden cells were transcriptionally closer to benchmark malignant than non-malignant compartments; in Visium data, sampled Hidden spots contained histologist-confirmed morphologically malignant cells. CATFISHR reduces missed malignant calls while preserving rare, transitional and benchmark-discordant populations for downstream investigation.
ER -